Administrative tasks for provisioning, change management, disaster recovery planning, problem determination, etc. are becoming increasingly application-centric where the goal is to provide service level agreements (SLAs) at the application level rather than individual layers of storage, servers, and networks. Growing virtualization of server-storage-networks is changing the way data centers have traditionally been managed and evolving into the “dynamic data-center” model where logical units of computation, storage, network bandwidth can be allocated and continuously changed at run-time based on the changing workload characteristics.
Traditionally, resource allocation decisions are done within individual tiers. For example, conventional allocation of storage involves finding a storage volume that can satisfy the capacity, performance, availability requirements. This approach, while simple, has limitations of possibly selecting a volume which has insufficient path bandwidth or an unreliable switch from the server to the selected storage volume.
The related work for resource optimization frameworks can be divided into four categories as shown in Table 1 below; each category has different pros and cons in representation of domain knowledge (referred to as “facts” in expert systems terminology) and the optimization formalism.
TABLE 1KnowledgeKnowledge UsageLimitations/Representation (facts)(formalisms)ChallengesPolicyEvent-Condition-Scanning forComplexity,BasedAction Rules “Cannedapplicable rulesbrittlenessRecipes”PureLittle or no informationIncrementallyInfeasible forFeedbackabout system details.explore differentproduction systemsBasedUse instantaneouspermutations withinwith a large solution-reaction as basis forthe state-spacespacefuture actionEmpirical/Recording systemFinding a recordedError-prone andLearningbehavior in differentstate that isinfeasible in real-Basedstates“closest” to theworld systems withcurrent statelarge number ofparametersModelMathematical or logicalOptimizing basedRepresentation ofBasedfunctions-- predictorson predictedmodels. Creation andof system behavior.system-state forevolution of models.Originally proposed fordifferentFormalisms forsystem diagnosticspermutations ofreasoning.parametersInaccuracies inpredicted values.